Intelligent drug delivery system

ABSTRACT

A controller and associated multi-axis sensor system for augmenting the automatic intelligent delivery of one or more drugs is provided. The controller and associated multi-axial sensor system are based on the detection and determination of particular physical lifestyle events. As a specific example, a pump augmentation system includes a six-axis accelerometer sensor, a gyroscopic pitch sensor and a controller. The controller is configured to receive motion data from the six-axis accelerometer sensor and orientation data from the gyroscopic pitch sensor. The controller provides a pump instruction signal for changing a delivery rate of a drug to a user based on the motion data and the orientation data. The system and methods are particularly suited for treating a user with Parkinson&#39;s disease.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation-in-part application of U.S. patentapplication Ser. No. 16/593,020, filed Oct. 4, 2019, which is herebyincorporated by reference in its entirety.

TECHNICAL FIELD

The present disclosure generally relates to a controller and associatedsensor system based on lifestyle event detection, and more particularlyrelates to a controller and associated sensor system for augmenting theautomatic delivery of drugs based on the detection and determination ofparticular lifestyle events (a Pump Augmentation System (PAS)). In oneaspect, the controller and associated sensor system relates to theoperation of insulin pumps, and more particularly relates to an InsulinPump Augmentation System (IPAS) for assisting insulin pump delivery ofinsulin to a user based on lifestyle event detection. In another aspect,the controller and associated sensor system relate to an IntelligentDrug Delivery System (IDDS) for collecting patient motion informationand providing a continuous medicinal infusion pump with patient motionfeedback information in order to effectuate a closed-loop dosagedetermination.

BACKGROUND

Despite the progress of diabetic management via an insulin pump, evenafter the introduction and integration of continuous glucose monitoringwith a “closed loop” approach, there still remains a disconnect betweenthe capability of conventional insulin pump systems to satisfactorilydetect and compensate for changing physiological, lifestyle, andexercise of an individual. All of these situations frequently result inunexpected raising and/or lowering of blood glucose levels, often timessuch that the blood glucose levels are outside of their desired,targeted or acceptable glucose ranges.

As an example, the everyday act of simply awakening for an individualtriggers a release of hormones that characteristically causes a person'sblood glucose level to rise. In a non-diabetic individual, the bloodglucose levels are organically adjusted so these situations gounnoticed. In a diabetic individual, however, there is no (or only alimited) physiological mechanism that recognizes and compensates forsuch circumstances. Currently, treatment of diabetes generally relies onan “after the fact” corrective measures to bring the changing bloodglucose levels back to normal ranges.

In contrast to the need to treat rising blood glucose levels, diabeticindividuals also continually face the opposite problem, even with“closed-loop” insulin pump therapy. Normal physical activity such asworking, walking, or exercising can frequently bring a diabeticindividual's blood glucose level down to dangerously low levels. Tocomplicate matters even further, in certain circumstances the samephysical exertion during a period of elevated blood glucose levels canactually further increase the blood glucose levels to a dangerousdegree.

At its core, the overall control problem results from the fact thatinsulin pumps follow a rigid, time of day based delivery process for thecontinuous, or basal, rate of insulin delivery, as well as only beingable to react to an abnormal glucose level after a deviation has alreadyoccurred, or is in the process of taking place.

At its best, conventional insulin pumps or closed-loop insulin pumps areinherently limited to indirectly reacting to changes in interstitialfluid glucose levels, which are in of itself a delayed measure of trueblood glucose levels. Present treatment methods lack the ability todynamically and automatically proactively increase or decrease aninsulin delivery rate, and therefore such methods merely treat theconsequential effects of lifestyle or physiological activity.

Unlike many clinical treatments that are guided by empirical clinicalmeasurements related to a patient's condition, Parkinson's disease doesnot have a defined laboratory test upon which to empirically judgeeither the progression of the disease and/or the true efficacy oftreatments. As a result, a Parkinson's disease clinician is forced todepend on either a patient's subjective self-evaluation of a change incondition and/or observations made during a clinical snapshot of thepatient in order to evaluate a patient's condition in an effort todetermine whether a medicine dosage adjustment is necessary in an effortto minimize or curtail abnormal movement symptoms. Unfortunately, suchmethods for basing dosage adjustments typically result in dosages thatare in excess of what is actually required, which in itself createssecondary complications.

The use of a conventional infusion pump to deliver Parkinson's diseaserelated medication may offer limited benefits to a patient such aspreventing missed doses and/or preventing inadvertent duplicate doses,but typically cannot be used to deliver medication doses when a patientis sleeping. Even with the use of a conventional infusion pump, there isstill great clinical uncertainty as to whether the correct/optimumdosage has been determined, especially during sleep periods when apatient is unable to provide self-observations and not usually able tobe observed in a clinical setting.

SUMMARY

According to a first aspect, a controller based on lifestyle eventdetection, and more particularly a controller for augmenting theautomatic delivery of drugs based on the detection and determination ofparticular lifestyle events may be provided.

For example, in one embodiment, an insulin pump augmentation system mayinclude a body, an accelerometer sensor, a gyroscopic pitch sensor, anda controller. The accelerometer sensor may be arranged on the body andconfigured to output motion data based on detected motion. Thegyroscopic pitch sensor may be arranged on the body and configured tooutput orientation data based on detected orientation. The controllermay be in communication with the accelerometer sensor and the gyroscopicpitch sensor. Further, the controller may be configured to receive themotion data and/or the orientation data. The controller may beconfigured to generate a pump instruction signal based on the motiondata and/or the orientation data. The pump instruction signal mayinclude a signal to change an insulin delivery rate of an insulin pump.

The signal to change an insulin delivery rate of an insulin pump may bea signal to reduce or increase the flow of insulin, to start a flow ofinsulin, to stop the flow of insulin, or to deliver an insulin bolusamount.

According to another embodiment, the controller may be configured toanalyze the motion data and/or the orientation data on a time weightedbasis. Further, the controller may be configured to utilize a datapattern matching algorithm to provide a determination of an occurrenceof a lifestyle event of a user. The data pattern matching algorithm mayutilize pattern data previously entered by a user. The pump instructionsignal may be based, wholly or partly, on the determined lifestyleevent.

The controller may also be configured to receive circulating insulinlevel data indicative of a level of insulin circulating within the user.The pump instruction may be based, wholly or partly, on the circulatinginsulin level data.

The controller may be configured to receive blood glucose level dataindicative of a level of blood glucose level within the user. The pumpinstruction signal may be based, wholly or partly, on the blood glucoselevel data.

The controller may be configured to analyze the circulating insulinlevel data and the blood glucose level data. The pump instruction signalmay be based, wholly or partly, on the analysis of the circulatinginsulin level data and the blood glucose level data.

According to another aspect, the controller may be configured to analyzethe circulating insulin level data and the blood glucose level data withregard to the determined lifestyle event. The pump instruction signalmay be based, wholly or partly, on the analysis of the circulatinginsulin level data and the blood glucose level data with regard to thedetermined lifestyle event.

According to a further embodiment, the controller may be configured toanalyze the motion data and/or the orientation data on a time weightedbasis. The controller may be configured to utilize a data patternmatching algorithm to compare the motion data with one or morepredetermined motion data patterns stored in the pump augmentationsystem. The controller may further be configured to determine at leastone of a type of food being ingested by the user, a quantity of saidfood being ingested by the user, and a resultant carbohydrate load beingingested by the user. The controller may be configured to determine atarget amount of insulin based on at least one of the determined type offood, the determined quantity of food, and the determined carbohydrateload. The pump instruction signal may be based, wholly or partly, on thedetermined target amount of insulin.

According to another embodiment, the controller may include a memory andbe configured to store in the memory the motion data and the orientationdata received by the controller during a lifestyle event of a user.Further, the motion data and orientation data may be associated with aspecific type of lifestyle event of a plurality of types of lifestyleevents.

According to one embodiment, the insulin pump augmentation system may beincorporated within, and operationally connected to either an internalor external insulin pump which is attached to the user.

According to one embodiment, the body of the insulin pump augmentationsystem may be a stand-alone wearable device configured to be worn by theuser. The wearable device may be configured to be worn on a limb, suchas on the arm at the wrist of the user, and is operationally connectedto either an internal or external insulin pump which is attached to theuser.

According to another embodiment, the insulin pump augmentation systemmay include a microphone configured to detect audio and to output audiodata based on the detected audio. The controller may be configured toreceive the audio data. The pump instruction signal may be based, whollyor partly, on the audio data received by the controller.

According to even another embodiment, the controller may include amemory and be configured to store in the memory certain previouslystored audio data received by the controller during ingestion of food.The controller may also be configured to receive an identifying inputfrom the user to define and match a particular food type of the foodpreviously ingested and matched to the audio data received by thecontroller. The controller may be configured to store the audio data inassociation with the particular food type indicated by the input.Further, the controller may be configured to determine a particular foodtype based on the stored audio data.

The controller may be configured to store, into a memory, the motiondata and the orientation data received by the controller during aprevious ingestion of a particular type of food. In the memory, themotion data and orientation data may be associated with a particularfood type of a plurality of food types.

The controller may be configured to receive a selection from the user ofphysical ingestion characteristics of the particular food type based onstored motion data and orientation data or other physical ingestioncharacteristics.

According to an embodiment, the controller may be configured to select aparticular food type from a plurality of food types, stored in a memory,based on at least one of the motion data, the orientation data and theaudio data. The controller may further be configured to generate thepump instruction signal based on the selection of the particular foodtype.

The controller may be configured to estimate a quantity of caloriesingested by a user and maintain a running caloric count representativeof a sum of calories ingested by the user throughout a time period.Further, the controller may be configured to generate a signal when thesum of calories ingested by the user is greater than or equal to apredetermined caloric threshold.

The controller may be configured to estimate a quantity of carbohydratesingested by a user and maintain a running carbohydrate countrepresentative of a sum of carbohydrates ingested by the user throughouta time period. Further, the controller may be configured to generate asignal when the sum of carbohydrates ingested by the user is greaterthan or equal to a predetermined carbohydrate threshold.

According to another embodiment, the insulin pump augmentation systemmay include an indicator emitting device configured to emit a sound(s)or vibration. The controller may be operatively connected to theindicator emitting device and configured to increase the sound leveland/or vibration level and increase a duration of the sound and/orvibration when the controller determines a user is non-responsive toacknowledging the sound or vibration.

The controller may be operatively connected to a communication device,and may be configured to cause the communication device to be activatedwhen the controller determines the user is not responding to anescalating series of alarm sounds or vibration. Optionally, when thecommunication device is activated, an emergency call or message may besent that includes real-time medical information relevant to the userand/or location information.

According to another aspect, a pump augmentation system may include abody, at least a six-axis accelerometer sensor, a gyroscopic pitchsensor, and a controller. The six-axis accelerometer sensor may bearranged in or on the body and configured to output motion data based ondetected motion. The gyroscopic pitch sensor may be arranged in or onthe body and configured to output orientation data based on detectedorientation. The controller may be operatively connected to the six-axisaccelerometer and the gyroscopic pitch sensor and configured to receivethe motion data and/or the orientation data. The controller may beconfigured to generate a pump instruction signal based on the motiondata and/or the orientation data, wherein the pump instruction signalmay include a signal to change a material delivery rate of a pump.

According to another aspect, a method of augmenting a pump systemincludes monitoring motion data and/or orientation data, and generatinga pump instruction signal based on the motion data and/or theorientation data. The pump instruction signal may include a signal tochange a material delivery rate of a pump. The pump system may include adevice body, an accelerometer sensor arranged in or on the device bodyand configured to output the motion data based on detected motion, agyroscopic pitch sensor arranged in or on the body and configured tooutput the orientation data based on detected orientation, and acontroller connected to the accelerometer and the gyroscopic pitchsensor, the controller being configured to receive the motion dataand/or the orientation data. The controller may perform the generating apump instruction signal.

According to another aspect, an intelligent drug delivery system (IDDS)and method uniquely provides an objective analysis of drug efficacy overtime because the system can continuously monitor any physical symptomsthat a patient presents and continuously analyze the effect of variousdosages even while a patient is asleep and, therefore, incapable ofmaking any subjective observations during a large portion of the day,e.g. one-third of the day.

An IDDS according to the present disclosure uniquely provides a way toquantitatively measure and evaluate a patient's symptoms through thepatient wearing a sensing device of the IDDS on their wrist(s) and/orother parts of their body. The sensing device(s) may be used in astand-alone manner for the purpose of supplying a physician with instantand/or archived empirical data for a clinician or physician to basesubsequent dosage decisions for either oral or infusible medication. Thesensing device(s) can be used with an operative connection to acontinuous medical infusion pump to form a closed loop system.

These and other objects, features and advantages of the presentinvention will become apparent in light of the description ofembodiments and features thereof, as illustrated and enhanced by theaccompanying diagrams.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows an exemplary pump for a pump augmentation system (PAS)(e.g., an IPAS-enabled insulin pump or an IDDS-enabled drug deliverypump) in accordance with embodiments of the present disclosure;

FIG. 2 shows an exemplary PAS-enabled wrist-worn device (e.g., anIPAS-enabled wrist-worn device or an IDDS-enabled wrist-worn device) inaccordance with embodiments of the present disclosure;

FIG. 3 shows an exemplary flow diagram for the operation of the IPAS ofFIG. 1 in accordance with embodiments of the present disclosure;

FIG. 4 shows an exemplary flow diagram for the operation of the IPAS ofFIG. 1 in accordance with embodiments of the present disclosure;

FIG. 5 shows an exemplary flow diagram for the operation of the IPAS ofFIG. 1 in accordance with embodiments of the present disclosure;

FIG. 6 shows an exemplary flow diagram for the operation of the IPAS ofFIG. 1 in accordance with embodiments of the present disclosure; and

FIG. 7 shows an exemplary flow diagram for the operation of the IDDS ofFIGS. 1 and 2 in accordance with embodiments of the present disclosure.

DETAILED DESCRIPTION

Before various embodiments are described in further detail, it is to beunderstood that the present disclosure is not limited to the particularembodiments described. It will also be understood that the methods andapparatuses described herein may be adapted and modified as appropriatefor the application being addressed and that the devices, systems andmethods described herein may be employed in other suitable applications,and that such other additions and modifications will not depart from thescope thereof.

Although various features have been shown in different figures forsimplicity, it should be readily apparent to one of skill in the artthat the various features may be combined without departing from thescope of the present disclosure.

Certain features and elements of the Pump Augmentation Systems (PAS)described below are applicable to an Insulin Pump Augmentation System(IPAS) for treating diabetes and/or to an Intelligent Drug DeliverySystem (IDDS) for treating diseases such as Parkinson's disease. An IPASis configured for delivering insulin, while an IDDS may be configured todeliver an anti-tremor medication or other medication for treating apatient with Parkinson's disease.

According to certain aspects, a controller associated with a PumpAugmentation System (PAS) or with an Insulin Pump Augmentation System(IPAS), in accordance with the present disclosure, provides improvedpump operation or insulin pump operation control schemes and devices andsystems for use with a pump or an insulin pump.

For example, the present disclosure provides an Insulin PumpAugmentation System (IPAS), which uniquely provides a closed-loopinsulin pump with an understanding of various physiological and/orlifestyle activities its user is undergoing in real-time, so as to allowfor dynamic proactive automatic compensation for said activities tobetter keep a diabetic individual's (or other individual) blood glucoselevel within a “desirable” target range. This proactivity is immenselyimportant, as the mechanical insertion of insulin into a body, whetherthrough a manual injection process or through an insulin pump, does notconfer the same immediate glycemic response to a diabetic individual'sglucose level, as compared to what a “normal” (non-diabetic) organicsolution would provide.

There is a time lag between changes in blood glucose levels and whenthose changes are subsequently reflected by interstitial fluid readings,and as a result the existing “after the fact” insulin delivery reactionand correctional methods to external lifestyle factors typically resultin undesirable “out of range” conditions. While insulin pumps aregenerally able to deliver different preset basal insulin infusion(delivery) rates based upon fixed, predetermined time schedules, theserates are not able to take into consideration the typical variations toan insulin pump user's varying schedule.

As an example, with the rise in glucose levels that diabetic individualsexperience upon awakening as a result of the “Dawn Phenomenon,” the bestthat current technology can do is to be statically programmed to allowfor an increased basal insulin delivery during a specific pre-set timeframe. The obvious problem with this method, however, is that unless anindividual exactly conforms to the strict time schedule corresponding tothe expected insulin delivery increase, this timed increased insulindelivery level will not match the actual glucose level insulinrequirement, and can result in abnormally low or high blood glucoselevels.

Given the reality of the numerous every-day “real-world” variations,precisely achieving both an exact time to bed, as well as an exact timeof awakening in order to maintain consistent sleep durations in order topredict when a hormone release should occur is almost impossible toachieve consistently. Since present-design insulin pumps do not have theability to recognize changes in sleep patterns (such as is experiencedwith variations between weekday sleep schedules and weekend or vacationsleep schedules, etc.), there is an inherent timing issue with thepresent treatment methods which prevent the proper response to the “DawnPhenomenon” blood glucose changes.

In a similar fashion, conventional insulin pumps have no way toproactively react to spontaneous physical activity, whether it is duringemergency situations (such as having to run down numerous flights ofduring an emergency evacuation), or for pleasure (such as a spontaneousextra round of tennis or other sporting activity that was not previouslyenvisioned), and as a result even a closed-loop insulin pump crudelyattempts to reactively reduce or suspend insulin delivery after the factin order to maintain control.

Where ‘conventional’ (non-PAS enabled or non-IPAS-enabled) ‘smartwatches’ or similar devices are capable of generating reports or savinginformation about the total number of strides or other physicalactivity, these devices only display prior information such as caloriesburned and distances covered, and are not configured for automaticresponsive action based on such information for controlling or modifyingan insulin pump. In contrast, according to an aspect of the presentdisclosure, a controller (for example, a controller integrated into anIPAS) uses sensed data to automatically determine and provide beneficialchanges to the insulin delivery rate of an insulin pump (e.g. increasesand/or decreases of insulin delivery rate). The novel use of an InsulinPump Augmentation System (IPAS), which may, for example, incorporate oneor more six-axis accelerometer/gyroscopic pitch sensors (or other numberof axis accelerometer(s)), and whose data output may be processed andanalyzed in real-time through an artificial intelligence softwareprogram, for the first time gives a closed-loop insulin pump the abilityto have physiological and/or physical situation awareness in order tobetter match insulin delivery levels to a body's actual insulinrequirements. IPAS is also ideally suited for common problematicsituations, where insulin-pump users either forget to temporarilysuspend the insulin delivery of their pump beforehand and/or duringexercise, and/or forget to bolus for a planned or ingested carbohydrateload. Both aforementioned situations can result in potentially seriousblood-glucose excursions developing, which may be avoided through theuse of IPAS according to embodiments of the present disclosure.

For the purposes of present disclosure, the term “lifestyle” includesevents such as: a person's current sleep state (e.g., determining if aperson is awake or sleeping); a person's current exercise or physicalmotion state (or movement state); a person's current food intake state(eating, chewing, drinking, etc.); and a person's real-timeidentification of a specific food currently being ingested as well asthe total quantity of that food having been ingested. A “lifestyle”situation awareness augmentation provided to an insulin-pump by an IPASallows a closed-loop insulin pump to monitor and automatically correctfor physical exertion activity which may change a user's glucose level,a ‘real-time’ monitoring, identification, and insulin compensation for arange of ingested food, and the ability to monitor an individual'ssleep/awake status and compensate for end-of-sleep hormone releasechanges to a user's blood glucose level.

According to certain aspects, a controller is associated with an InsulinPump Augmentation System (IPAS). An IPAS can be configured in differentphysical embodiments, with three exemplary embodiments including:

1) IPAS components being completely integrated into the body of anIPAS-enabled insulin pump. The data from the pump's integrated IPASsensors being processed by an associated Artificial Intelligencesub-system within the IPAS, and the resultant guidance being provided tothe insulin pump's delivery system(s) to act upon (or a controller ofthe insulin pump delivery system(s)).

2) The configuration of embodiment #1, further augmented by theadditional use of a physically separate second six-axis accelerometerand gyroscopic pitch sensor, and a microphone that is integrated into awrist-worn device that is placed on an insulin-pump user's arm or wrist(preferably the dominant arm or wrist), either as an IPAS-enabledsmart-watch or as a proprietary IPAS wearable device. The data outputfrom these additional sensors is transmitted to an IPAS-equipped insulinpump (e.g. through wireless communication means), where the added datastream is combined with the data supplied from the pump's integralsensor(s), with both data streams subsequently being processed by anArtificial Intelligence sub-system within the insulin pump.

3) A configuration for use with a non-IPAS enabled closed-loop insulinpump. In this configuration, all of the IPAS sensing elements as well asthe IPAS data processing functions are physically integrated into thebody of a “smart-watch” or other IPAS wearable device which is worn onthe wrist or arm of an insulin pump user (e.g. the dominant wrist orarm). An integral six-axis accelerometer and gyroscopic pitch sensor,along with a microphone are used as input devices. In this embodiment,the IPAS-enabled arm-worn device itself calculates supplemental insulinand/or delivery modification instructions, and then wirelessly transmitssaid instructions to a ‘conventional’ insulin pump, i.e. non-IPASequipped insulin pump, for execution. Said insulin pump would alsotransmit “real-time” parameters such as blood-glucose levels andcirculating insulin to the IPAS device.

In a first example of what the newly-found lifestyle awareness conveysto an insulin pump, the augmented pump will now be capable ofdynamically determining a sleep state or an awakened state of a user,and proactively make compensating adjustments to an insulin deliveryrate commensurate with an estimated body's release of hormones uponawakening. With this new lifestyle awareness, the glucose loweringeffect of insulin can now be better timed to match the escalatingblood-glucose effects of a hormone release upon awakening with acommensurate insulin release upon the IPAS sensing the physicallyawakened state of an insulin-pump user.

In accordance with embodiments of the present disclosure, a controllerassociated with an IPAS may determine that an individual is in asleeping state or non-sleeping state based on the individual's physicalorientation. For example, whether said individual is physically orientedin a position characteristic of sleep along with a reduced state ofmotion for an extended period of time, an IPAS, through its six-axisaccelerometer(s) and gyroscopic pitch sensor(s) can similarly recognizethe physical positioning and a long-term lack of motion of anindividual, and match this data with a stored template indicative of asleeping state for that individual. Conversely, when the controllerassociated with the IPAS detects that an individual has changed from alimited motion, long duration sleeping state position to an uprightposition through detected multi-axis motion, then a determination of anon-sleeping state position (or awakening state or morning awakening) bythe IPAS can also be made. The IPAS Artificial Intelligence subsystemcan determine through an algorithm that, for example, includes atime-weighted motion analysis (to prevent short duration awakening frombeing incorrectly interpreted as morning awakening), as well asobserving the range and speed of detected motion, in order to filter outthe typical transient movement and position shifting of an individualduring various sleep phases from an actual awake state.

With the inherent ability of the controller associated with the IPAS todetect the sleeping state of a user, IPAS has the capability to not onlyadvance or delay the pre-programmed time-based basal rates, but it canalso correct and align the fixed/preset basal rates with actuallyobserved conditions such as is encountered when sleeping or travellingbetween time zones. Similarly, an IPAS according to embodiments of thepresent disclosure can temporarily skew a current basal rate to betteralign with instant or predicted near future bodily insulin requirements.

An IPAS's sleep/awake determination can also provide criticaluser-condition determination and responses. In the instance of thecontroller associated with an IPAS-equipped insulin pump sensing anabnormally low (or excessively high) blood glucose reading while itsuser is presumed sleeping, it can both increase the volume levels of awarning alarm(s), as well as the duration of such alarms beyond itsusual daytime parameters, as arousing a sleeping person experiencing lowblood-glucose levels can be especially challenging. In some embodiments,in the event that an IPAS-equipped insulin pump, after the completion ofan enhanced alarm sequence(s) does not sense an awakened condition, thecontroller associated with the IPAS is configured to presume that theuser has lost consciousness (or is otherwise non-responsive) andautomatically commands a nearby mobile device to place an emergency callfor medical help. Simultaneously, the controller associated with theIPAS may cause an insulin pump to automatically suspend insulindelivery. In the event of a non-responsive individual along with anobserved extremely low blood-glucose indication, IPAS may be configuredto automatically suspend insulin delivery and/or deliver an infusion ofglucose-raising medication such as Glucagon. In the case of excessivelyhigh blood glucose levels, IPAS may be configured to automaticallydeliver an appropriate insulin bolus to avoid or correct a ketonicsituation. For example, the controller associated with the IPAS couldcommand the nearby mobile device through Bluetooth wirelesscommunication or the like. Even though the unconscious individual may beunable to speak, a digitized voice message would indicate to anemergency operator the nature of the medical emergency, as well as relaythe location of the user through GPS or other location informationtechnology of the mobile device should that information not be availablevia enhanced 911 systems. Optionally, IPAS could deliver to theemergency operator the observed blood-glucose levels for more timelysituation awareness and action by first responders.

In a second example, a non-IPAS enabled insulin pump is completelyignorant as to the ‘moment to moment’ lifestyle activities of its user,and thus has no ability to proactively deviate from its preset deliverysettings. It is well known that physical activity affects a diabeticindividual's blood glucose levels, either by lowering a diabeticindividual's blood-glucose level or raising it depending on what theblood glucose level is at the time of said exercise. By the use of anintegrated multi-axis accelerometer and pitch sensing sensor (orseparate adjunct multi-axis accelerometer and gyroscopic pitch sensors),an insulin pump is able to gain a continuous (or semi-continuous)insight into a user's exercise/physical activity, and dynamically andproactively adjust the user's insulin delivery rate accordingly, ratherthan attempting to reactively correct a resultant change in bloodglucose level as accomplished through conventional insulin pump devices.

According to embodiments of the present disclosure, an IPAS may achieveimprovements in a user's “in-range” glucose readings. Changes inexercise or physical activity can now be immediately detected (or nearlyimmediately) allowing for contemporaneous alteration to a user's basalinsulin rate immediately (or near immediately) upon the initiation ofexercise. Artificial Intelligence (A.I.) logic may be employed to bothanalyze a user's current blood-glucose levels as well as determine the(presently) circulating insulin levels (as provided by the insulin pump)to make appropriate insulin adjustments by the pump as needed. If theinstantaneous circulating insulin level at the time of exercise isdeemed adequate and the detected blood glucose level is within a‘normal’ range (or within a predetermined range), then the controllerassociated with the IPAS may be configured to bias the insulin pump tostop or lower the insulin delivery basal rate commensurate with thesensed level and duration of exercise. If at the time of exercisecommencement, the detected blood glucose level is well above normal (oris above a predetermined threshold) and/or there is a low level ofcirculating insulin (or is below a predetermined threshold), then thecontroller associated with the IPAS may be configured to cause theinsulin pump to either bolus and/or increase its basal rate tocompensate for the exercise so as to prevent a further increase in bloodglucose levels caused by the exercise.

A determination of exercise or other strenuous activities (such asplaying tennis) may be made through an A.I. motion algorithm, which maybase the determination on how many active axes are reporting motionabove a predetermined motion threshold level, the excursion ranges ofsaid reporting axes, and any repeating cadence patterns (to detectrunning or other specific activities). The algorithm may be designed tofilter out ‘false’ exercise reporting situations, such as when anindividual is riding in a car (repeated rising up and down) so as not toconfuse said vertical ‘bouncing’ up and down with the vertical motionsone might associate with running. The motion algorithm would note thatwhile there were vertical (and potentially other motions), the excursiondistances were limited from what one would expect from exercise, withforward movements and other axis readings missing along with a verydifferent cadence pattern.

The Insulin Pump Augmentation System (IPAS) is not limited to thelifestyle examples described herein. Conventional insulin pumps have nodirect means for lifestyle awareness, and as a result of thisdeficiency, the pump is completely unaware of a user's food ingestion.Without a “real-time” method to sense an ingested carbohydrate load,conventional closed-loop pumps are oblivious to food being ingested, andmerely indirectly and reactively sense that the user's blood glucoselevels are rising toward or beyond a target range or rate beforeinitiating corrective action. Even in the case where an insulin-pumpuser manually boluses insulin prior to food consumption, this action isjust a guess as to how much carbohydrate may or may not subsequently getconsumed.

With the present invention, an IPAS-equipped insulin pump is not onlycontemporaneously presented with real-time information indicative offood ingestion, but in many cases, even the precise type of food, theactual quantity of food consumed, the resultant calculated ingestedcarbohydrate load, the glycemic index of said food, and a compensatinginsulin bolus amount and release timing for that ingestion may beestimated and provided to the pump. This allows an insulin pump toimmediately (or near immediately) and contemporaneously proactivelymatch supplemental insulin dosages to the amount as well as type of foodbeing consumed, as opposed to a non IPAS-equipped pump needing toreactively compensate for said food ingestion in an imperfect “after thefact” manner.

As an example, in the case of a person eating popcorn, the dominant hand(or non-dominant hand) wearing an IPAS sensor(s) repeatedly moves in adistinct pattern while taking food from a fixed “supply container” andbringing the popcorn to their mouth. By analyzing the data from one ormore of the six-axis accelerometer sensors and/or one or more of thegyroscopic pitch/yaw/roll sensors during this activity, a distinctiverepetition pattern allows the controller (for example, with anincorporated A.I. algorithm) to record the detected arm and handmovements, which can be accurately saved as a template representative ofthat particular food being ingested. The controller/A.I. system may notonly analyze the repeated multi-axial positional locations, speed, andcadence of said movements, but it may also generate a digitized audiofile from the wrist-mounted microphone when the wrist worn device isdetermined to be at the closest position to the mouth of the user. Theaudio file may further assist the A.I. algorithm in differentiatingbetween, for example, a person eating popcorn and a person eating potatochips by differentiating between their distinctive chewing sounds, aswell as the duration of chewing sounds.

By determining and analyzing the “linger” time that a hand is held at ornear a mouth, as well as the number and type of sequential angularmovements of the hand and wrists before moving away from the mouth, thisallows a further quantitative determination of what has been ingested,and by supplemental simple calculation, the instantaneous carbohydrateingestion level for each such movement cycle may be calculated, and adetermination as to a compensatory insulin “bolus” is made. By infusinginsulin proximate (and at the appropriate compensating level) toingestion taking place, as tempered by the glycemic index of theidentified food, a superior proactive match of insulin and carbohydrateload may be made.

The variation in types of foods is quite extensive. By having anindividual “record” various foods being eaten, as well as data labelingof the recorded foods, a controller associated with an IPAS according toembodiments of the present disclosure can easily match and reference thecarbohydrate value, the glycemic index, calories, etc. to preciselytailor the delivery values necessary for an insulin pump to match theinsulin need and timing to compensate for a glucose level increase inthe blood caused by that food when ingested.

The physical motions and cadences typically associated with eatingvarious foods can be quite distinctive for certain foods. Examples ofthis include, without limitation, the eating an ear of corn, the eatingan apple (with distinctive ‘snap back’ after each bite), the licking ofan ice cream cone, the peeling and eating of a banana, etc. Because ofunique physical movements while eating and/or eating sounds associatedwith each food, a series of specialized food templates may be generatedand/or pre-stored in the controller associated with the IPAS.Additionally, manually inputting the food type into the IPAS by a user,for example by pressing a button on the wrist-worn device and speakingthe name of a particular food, the carbohydrate and caloric informationmay be recalled (or identified) for foods that have difficulty in beingautomatically recognized based on a food template, and the IPAS willmonitor the ingestion amount to resolve (or determine) a carbohydrateload and insulin bolus. For low-carbohydrate food such as meat, the IPASmay recognize the unique motions of meat cutting before ingesting (ifthe user prepares the food for ingestion).

The sound characteristics of not only the actual ingestion of abeverage, but also the sounds (or lack of) created by the actual bottleor container may be especially important in identifying what thebeverage (and its carbohydrate content) is. For example, disposableplastic water bottles, since they do not need to handle the pressures ofcarbonation, are typically constructed of much thinner plastic materialthan ‘soda’ bottles, and as such they produce a characteristicallyunique “plastic flexing/crackling” sound when handled and being consumedfrom. This unique plastic water bottle sound would be used by IPAS todetermine that a non-caloric/zero carbohydrate ingestion was takingplace. Conversely, a carbonated beverage would use a different type ofbottle as well as producing different ingestion/carbonation sounds. Withregard to determining whether the carbonated beverage is “diet” orregular (with their corresponding vastly different carbohydrateamounts), the IPAS algorithm merely needs to analyze the user's bloodglucose level at the time of ingestion to make a logicaldifferentiation. Since an IPAS user is presumed to be a diabetic, thenunless the user's glucose level was low at the time of ingestion (whichwould make the ingestion of a “regular” soda or the like desirable inthat situation), then the beverage is always assumed to be “diet”.

Every food when ingested has a unique combination of positional androtational presentations to the mouth, a distinct biting pattern andsound, juice sucking sounds, chewing noises, hand retraction rotationand positioning, etc.

In some embodiments, the controller associated with the IPAS may notdirectly identify a type of food based on a pre-stored food template,but rather using a matching process wherein individual food templatesare recorded and saved by the user during ingestion, with the user thenmanually identifying and registering each different food. Subsequentfood identification may be accomplished by the IPAS automaticallycomparing in real-time active food ingestion with the saved digitalmotion and sound patterns of the saved templates. When a match is made,the carbohydrate levels, glycemic indices, caloric information, andother information is provided to the insulin delivery system fordetermining insulin delivery amount(s) and timing of said deliveryamount(s).

The algorithm that the controller associated with the IPAS used todetermine ingested food types may incorporate one or more validationmethods to increase accuracy. One of these methods is to only allow foodaudio matching (as captured by the wrist-worn device) during periodswhen the IPAS motion analysis determines that a user's hand is in aposition proximate to their mouth. By the use of such ‘audio gating’,the IPAS may prevent false analysis when multiple people are eatingeither the same type, or other food types in close proximity to thesubject IPAS. Said audio gating also inherently provides a level ofprivacy due to the wrist microphone being muted whenever the system doesnot detect a hand being raised and brought proximate to the mouth.Accordingly, the IPAS may not record audio data from the microphone whenthe IPAS is determined to not be at a proximate position to a mouth ofthe IPAS user, or disregard the recorded audio data if the IPAS isrecording the audio data.

In some embodiments, the controller associated with the IPAS isconfigured to initially contain a number of ‘generic’ motion templatesto immediately allow for recognition of awakening, running, or otheractivity. The IPAS is configured to not only allow a user to generateand replace said ‘generic’ templates with their own customindividualized templates to further increase both event recognition andaccuracy, but also to supplement the range of stored templates. Thecustom personalized templates may also replace the generic motiontemplates or be used in addition thereto for recognition of awakening,running, or other activity.

Another benefit of a controller associated with an IPAS according toembodiments of the present disclosure comes into play during episodes ofhypoglycemia. In some circumstances, individuals typicallyover-compensate their carbohydrate ingestion to treat the immediatesymptoms of hypoglycemia. In some embodiments, the IPAS is configured tomonitor an instant blood-glucose level, the amount of circulatinginsulin, as well as the amount of carbohydrate being ingested. Thecontroller associated with the IPAS may be configured to provide theuser with an ‘overshoot’ protection alert to guard against excessivecarbohydrate ingestion subsequently resulting in hyperglycemia. By theIPAS comparing said ingestion against both the instant glucose level aswell as the amount of circulating insulin, it can calculate (ordetermine) the appropriate amount of carbohydrate needed to normalizethe user's blood-glucose level by monitoring the instant carbohydrateingestion and sound an alert at a point of over-compensation.

In some embodiments, the controller may be associated with a PumpAugmented System (PAS). The PAS may also be used for delivery of otherinfusible medications or other infusible materials, such as, for exampleand without limitation, medications for treating Parkinson's disease. Inthis usage example, infused medication delivery time(s) and amount(s)can be matched with an instant need, as determined, for example, by anincreased tremor level which a PAS would detect.

For the purposes of the present disclosure, while the primary disclosedapplication is for the augmentation of an insulin pump, the same orsimilar hardware configuration, with minor software modification, mayalso be used for other purposes. In a further embodiment of thecontroller, the automatic food ingestion sensing may also be used as an‘ingestion’ caloric monitor as opposed to current devices that onlyreport calories that were ‘burned’, rather than consumed. In someembodiments, a controller configured for caloric ingestion monitoringmay optionally be configured to provide tactile or visual alarms orother guidance notification once a target caloric ingestion has beenachieved or failed to be achieved by a certain time of day.

The present disclosure provides a controller associated with a systemfor sensing and determining “lifestyle” activities.

According to the present disclosure a Pump Augmentation System (PAS) 10for sensing and determining “lifestyle” activities of a drug deliverypump user is provided. Referring to FIGS. 1 and 2, the PAS 10 could bespecifically implemented as an Insulin Pump Augmentation System (IPAS)or as an Intelligent Drug Delivery System (IDDS).

According to certain specific embodiments, an Insulin Pump AugmentationSystem (IPAS) for sensing and determining “lifestyle” activities of aninsulin pump user is provided. As shown in FIG. 1, the Insulin PumpAugmentation System 10 in accordance with embodiments of the presentdisclosure is integrated into, and/or operatively in communication witha drug delivery pump, such as an insulin pump, 100 having a pump body12. The IPAS 10 includes a controller 14, an accelerometer sensor 16, agyroscopic pitch sensor 18, an indicator emitting device 20 and atransmitter 22. The accelerometer sensor may be a multi-axisaccelerometer sensor, for example, a six-axis accelerometer sensor.

The controller 14 is operatively connected to the six-axis accelerometersensor 16, the gyroscopic pitch sensor 18, the indicator emitting device20 and the transmitter 22. While the controller 14 is shown as beingphysically connected to the six-axis accelerometer sensor 16, thegyroscopic pitch sensor 18, the indicator emitting device 20 and thetransmitter 22, the controller 14 may be “connected” to these elementsthrough wireless communication methods and connections.

The six-axis accelerometer sensor 16 is configured to detect motion (ormovement) and output motion data (or movement data). The controller 14is configured to receive and/or record or store the motion data from thesix-axis accelerometer sensor 16. The gyroscopic pitch sensor 18 isconfigured to detect orientation and output orientation data. Thecontroller 14 is configured to receive and/or record the orientationdata from the gyroscopic pitch sensor 18. The indicator emitting device20 is configured to emit one or more sounds (for example, an alarmsound) at various sound levels and/or to display one or more visualindicators (for example, a flashing light). The controller 14 isoperatively connected to the indicator emitting device 20. Thetransmitter 22 is configured to communication with one or morecommunication devices.

The controller 14 is configured to communicate with the insulin pump 100and receive various insulin pump 100 data. For example, and withoutlimitation, the controller 14 may receive circulating insulin level dataof a user of the insulin pump 100, a reported blood glucose level dataof the user of the insulin pump 100 and/or a current or scheduledinsulin delivery rate data of the insulin pump 100. The controller 14 isoperatively connected with a host insulin pump 100. The controller 14 isalso operatively connected to the transmitter 22 to cause thetransmitter 22 to trigger an automatic emergency call or message if oneor more predetermined criteria is satisfied based on the motion data,orientation data, audio data, circulating insulin level data, reportedblood glucose level data and/or current or scheduled insulin deliveryrate data.

As shown in FIG. 2, the IPAS 10 is further integrated into, and/oroperatively in communication with a wearable device 24. In thisembodiment, the wearable device 24 is a wrist worn device. The IPAS 10includes a six-axis accelerometer sensor 25 and gyroscopic pitch sensor26 located in the wearable device 24. The IPAS 20 further contains amicrophone 28. The microphone 28 is configured to detect audio andoutput audio data. The controller 14 is configured to receive the audiodata from the microphone 28 and/or the wearable device 24 includes anadditional controller(s) which is configured to distribute the motiondata, orientation data and/or audio data to the controller 14. It shouldbe readily understood that the microphone 28 may be arranged in otherpositions of the wearable device 24 and/or there be additionalmicrophones.

In some embodiments, the controller 14 is arranged in or on the wearabledevice 24. In some embodiments the controller 14 is arranged in or onthe insulin pump 100 as shown in FIG. 1, and a second controller isarranged in or on the wearable device 24. The second controller beingconfigured to communicate with the first controller 14 and/or adedicated controller of the insulin pump 100.

Referring to FIG. 3, a flow diagram 30 shows an exemplary method ofoperation of the IPAS 10 of FIG. 1 in accordance with embodiments of thepresent disclosure. At block 32, the controller 14 monitors for motiondata received from one or more of the six-axis accelerometer sensors 16,25 and for orientation data from one or more of the gyroscopic pitchsensors 18, 26. At block 34, the controller 14 determines if the motiondata and/or orientation data has changed. If the controller 14determines that there is no change in the motion data and/or orientationdata, then the controller 14 returns to block 32 for monitoring. If thecontroller 14 determines there is a change in the motion data and/ororientation data, the controller 14 proceeds to block 36 where thechange in motion data and orientation data is analyzed by the controller14. The controller 14 proceeds to block 38 where the controller 14compares the motion data and orientation data, which may betime-weighted, for similarities with a profile template stored in theIPAS 10. If the controller 14 determines that the motion data and/ororientation data is not similar to a stored profile template, thecontroller 14 returns to block 32 for monitoring. If the controller 14determines that the motion data and/or orientation data is similar to astored template, the controller 14 determines that the motion andorientation detected is associated with the ingestion of food andproceeds to the identified profile template method.

Referring to FIG. 4, a flow diagram 39 shows an exemplary profiletemplate method of operation of the IPAS 10 of FIG. 1 when the motiondata and/or orientation data is determined as being similar to a foodingestion profile at block 38 (FIG. 3) in accordance with embodiments ofthe present disclosure. The controller 14 proceeds to block 40, wherethe controller 14 optionally receives audio input 42 from the microphone28 (FIG. 2). The controller 14 compares the motion data, orientationdata and/or audio data corresponding to the time period determined to becontemporaneous with ingestion of food for a similarity with one or morestored food ingestion templates. If the controller 14 determines thatthe data is similar to a stored food ingestion template, the controllerproceeds to block 48, which is discussed in greater detail later herein.If the controller 14 determines that the data is not similar to a storedfood ingestion template, the controller proceeds to block 44 andrequests user input for food type of food being ingested. If no userinput is received, the controller 14 returns to block 32 for monitoring.If user input is received, the controller 14 proceeds to block 46 andstores the motion data, orientation data and/or audio data ascorresponding to a new food ingestion template of the input provided.The new food ingestion template is stored (e.g. in a memory associatedwith the controller) for future food ingestion template comparisons atblock 40. Then the controller 14 proceeds to block 48.

At block 48, the controller 14 determines a particular food typeidentified as being indicative of the food being ingested by the user.The controller 14 proceeds to block 50 where the controller 14 checksfor a reported blood glucose level of the user (e.g. by querying theinsulin pump 100). If the reported blood glucose level is greater thanor equal to a predetermined threshold (e.g. 100 mg/dl), then thecontroller 14 generates a pump instruction signal at block 52 causingthe insulin pump 100 to bolus insulin to the user based on the amount ofcarbohydrate load ingested (or consumed) as determined by the controller14, thereby changing the current or scheduled insulin delivery rate ofthe insulin pump 100. If the reported blood glucose level is less than apredetermined threshold (e.g. 100 mg/dl), then at block 54 thecontroller 14 checks a circulating insulin level within the user (e.g.by querying the insulin pump 100). If the circulating insulin level isbelow a predetermined threshold, at block 56 the controller 14 generatesa pump instruction signal to bolus insulin to the user based on theamount of carbohydrate load ingested (or consumed) as determined by thecontroller 14. If the circulating insulin level is above a predeterminedthreshold, then at block 58, the controller 14 does not generate a pumpinstruction signal or generates a pump instruction signal that reducesthe insulin delivery rate from the current or scheduled insulin deliveryrate. Then the controller 14 returns to block 32 for monitoring.

Advantageously, the controller 14 being configured to request andreceive input from a user at blocks 44, 46 allows the controller 14 torepeatedly learn the physical characteristics and/or mannerisms uniqueto the user. The stored patterns are individualized to the user allowingthe controller 14 to identify food templates (or other templates) moreaccurately. The controller 14 may be configured to store any number oftemplates input by the user giving the controller 14 the ability tostore virtually infinite patterns unique to the user. The ability tostore patterns unique to the user advantageously allows for thecontroller 14 to “learn” the user tendencies (or previously enteredpattern data) that correspond to a food template (or other template).For example, a user may tend to generate one or more unique motions,orientations or sounds when engaging in a physical lifestyle event thatthe controller 14 can identify as a particular template once stored.Thus, when the user again engages in that lifestyle event, such aseating potato chips in a particular physical manner, the controller 14is configured to identify the lifestyle event and generate a pumpinstruction signal accordingly as disclosed herein.

Referring to FIG. 5, a flow diagram 60 shows an exemplary method ofoperation of the IPAS 10 of FIG. 1 when the motion and/or orientationdata is determined as being similar to an exercise profile at block 38(FIG. 3) in accordance with embodiments of the present disclosure. Atblock 62, the controller 14 compares the motion data and/or orientationdata for similarity with a particular exercise profile. At block 64, ifthe controller 14 determines that the motion data and/or orientationdata does not correspond to a particular exercise profile, then thecontroller 14 returns to block 32 monitoring. If the controller 14determines the data does correspond to a particular exercise profile,the controller 14 proceeds to block 66 where the controller 14 checksfor a reported blood glucose level. If the reported blood glucose levelis below a first threshold, the controller 14 proceeds to block 68 andgenerates a pump instruction signal instructing the insulin pump 100 tosuspend insulin delivery or decrease insulin delivery. If the reportedblood glucose level is above the first threshold but below a secondthreshold (e.g. 250 mg/dl), the controller 14 proceeds to block 70 andchecks a circulating insulin level within the user. If the circulatinginsulin level is below a first insulin threshold, the controller 14proceeds to block 72 and maintains the existing insulin delivery rate(or at least does not cause the insulin delivery rate to changesignificantly). If the circulating insulin level is above a secondinsulin threshold, the controller proceeds to block 74 and reduces theinsulin basal rate. Referring back to block 66, if the blood glucoselevel is greater than a third threshold, the controller 14 proceeds toblock 76 and determines an increase in an insulin delivery rate and/oran insulin bolus commensurate with the particular exercise profile, thecontroller 14 generates a pump instruction signal to cause the insulinpump 100 to deliver the determined commensurate insulin rate or bolus.

Similar to the method discussed above in connection with FIG. 4, thecontroller 14 may be configured to request that the user enter anexercise template to be stored as corresponding to a recorded exerciseprofile. In some embodiments, the controller 14 does not need to requestthat the user enter an exercise or food template. The user can enter thecorresponding template even when not requested by the controller 14. Theentered template is stored with recorded data from the IPAS 10. In someembodiments the recorded data stored as the template corresponds to thedata recorded during a predetermined amount of time before the enteringof the template by the user, for example and without limitation, oneminute, two minutes or three minutes. In some embodiments, the user canchoose which recorded data is associated with the entered template. Forexample, the user can choose the amount of time prior to the entering ofthe template, or the user can choose a period of recorded data thatoccurred earlier in the day, e.g. if the user played tennis from 1:00 PMto 2:00 PM, later that day at 6:00 PM when the user is not playingtennis, the user could choose the time playing tennis as being stored asthe tennis exercise profile or template.

Referring to FIG. 6, a flow diagram 78 shows an exemplary method ofoperation of the IPAS 10 of FIG. 1 when the motion and/or orientationdata is determined as being similar to a sleep profile at block 38 (FIG.3) in accordance with embodiments of the present disclosure. At block80, the controller 14 determines a sleep state is detected. Thecontroller 14 proceeds to block 82, where the controller 14 continues tomonitor the motion data and/or orientation data for a determination of asleep to awake transition, which may be based on a time-weightedevaluation of the data. If no sleep to awake transition is detected, thecontroller returns to block 80 to check the data is similar to a sleepprofile and, if in the sleep state, returns to block 82 for determiningif a sleep to awake transition has occurred. If the controller 14,determines that a sleep to awake transition has occurred (e.g. by themotion data indicating that the user is moving or walking, or theorientation data indicating that the orientation the IPAS 10 haschanged), then the controller 14 proceeds to block 84 to check for areported blood glucose level of the user. If the reported blood glucoselevel is below a first threshold, the controller 14 proceeds to block 86where the controller 14 determines that no insulin bolus is necessary tocompensate for hormone release associated with a transition to anawakened state as discussed herein. If the reported blood glucose levelis above a second threshold, the controller 14 proceeds to block 88where the controller 14 determines an increase in an insulin deliveryrate and/or an insulin bolus to compensate for the user hormone release,then the controller 14 generates a pump instruction signal to cause theinsulin pump 100 to release the appropriate insulin at the appropriatedelivery rate. Then the controller 14 returns to block 32 formonitoring.

In some embodiments, an IPAS 10 is located entirely in or on an insulinpump 100 (e.g. FIG. 1). In some embodiments, an IPAS 10 is located in oron an insulin pump 100 and in or on a wearable device(s) 24 (e.g. FIGS.1 and 2) and, as disclosed herein, the IPAS 10 elements in the wearabledevice 24 are configured to communicate and work with the IPAS 10elements in the insulin pump 100. In some embodiments, an IPAS 10 islocated entirely in or on a wearable device 24 (e.g. FIG. 2) and isconfigured to communicate and work with a non-integrated IPAS insulinpump (i.e. does not have any IPAS functionality by itself), where theIPAS 10 in the wearable device 24 supplements or overrides at least somecontrol of the non-integrated IPAS insulin pump functions so that theinsulin pump operates like an integrated IPAS insulin pump. In someembodiments, a user might wear one or more wearable devices containingIPAS 10 elements, for example and without limitation, a wearable deviceon each wrist of the user.

Advantageously, IPAS enabled pumps are configured to provide advantagesover non-IPAS enabled pumps. For example, without an IPAS providing aninsulin pump a real-time indication of a user's sleep status, aconventional insulin pump may generate unnecessary alarms without regardto context. As an example, some insulin pumps may keep track of theamount of remaining insulin in its reservoir, and at various insulinremaining levels the pump may sound an alarm indicating the situation.As a result of this, insulin pump users are often awoken to take certainactions such as to change reservoirs even though the situation is notyet critical and such actions are ill-advised to be done when just wokenup in the middle of the night. In some embodiments, an IPAS enabledinsulin pump may determine whether a user is sleeping, and, if an alarmis determined as being merely advisory rather than time orsituation-critical, the IPAS enabled insulin pump may delay such alarmsand/or notifications until the user is awake and/or until the status ofthe alarm becomes time-critical.

Features and elements of the PAS and IPASs 10 described above areapplicable to an intelligent drug delivery system (IDDS) 10 for treatingdiseases such as Parkinson's disease and, therefore, will not berepeated in detail here. Instead of delivering insulin, an IDDS (or PAS)may be configured to alter the delivery rate of an anti-tremormedication or other medication for treating a patient with Parkinson'sdisease.

Quantitative measurable factors are not available for Parkinson'sdisease as are available for other diseases, such as glucose level,blood oxygen concentration level, blood pressure reading, EKG reading,body temperature, etc. Parkinson's disease differs in that there is nodirect and continuous measurable universal reference values upon whichto alter medicine delivery rates. Clinicians typically judge theprogression of the disease based on visual observation(s) of a patient,and make subjective dosage changes therein.

The motion and/or orientation data collected by the IDDS device 10 isanalyzed by the controller 14 to determine the presence of even subtlechanges in physical movement symptoms. A high resolutionMicro-Electromechanical Sensor(s) (MEMS) of the IDDS 10, e.g. six-axismotion tracking device which may include a three-axis gyroscope with athree-axis accelerometer, which allows for fine resolution detection ofpatient motions, which include tremor symptoms. Such resolution providesfor accurate pitch, roll and yaw motion sensing capability in additionto the traditional three Cartesian coordinate axis measurements. Thus,an IDDS 10 device according to the present disclosure is configured, forinstance, to monitor motions of a wrist/hand that is in a resting (orfixed) position having an X/Y/Z axis position, but whose fingers arerhythmically active, which are detected by the sensor(s) of the device10, indicating a tremor incident of a detectable intensity and durationthat may be recorded and stored in a local or remote storage.Embodiments using more advanced or higher resolution sensors may also beused.

With the use of a body-worn or internal to a medication pump sensor, thedevice 10 may be configured to differentiate from a normal uprightposition and a prone resting/sleeping position.

Each Parkinson's disease patient has individualized physical and/orcadence tremor characteristics throughout each stage of the disease.Thus, the ability to record and store tremor incidents allows for thecomparison of motion for determining whether there is a change induration and/or intensity of tremors, either increases or decreases,beyond a predetermined threshold, e.g. an increase of 10% duration or anincrease of 25% force of tremor movements. In some embodiments, a device10 is worn on each wrist of the patient (and/or a device 10 on one ormore ankles of the patient). In some embodiments, a device 10 is worn ona general body location, such as on the torso.

A closed loop IDDS in operation with a patient, such as for treating apatient with Parkinson's disease, is configured to utilize the at leastone body worn (e.g. wrist and/or general body location) motion andorientation sensor to operationally supplement or completely replaceconventional infusion rate settings used by conventional non-intelligentcontinuous fusion pumps (e.g. fixed dosage delivery or particular timeof day delivery infusion rate settings).

The term “continuous” as it is used in the context of the presentdisclosure will be understood by those of skill in the art to notliterally mean drug delivery on a non-stop basis, but a continualrepetition of delivery doses, with the spacing between dosing and theduration of each dose being dosage variables. In a similar fashion, itmay not be advantageous to make observations of the patient literallyevery second as this non-stop observation may not provide significantlymore data benefits as compared to data sampling with some length of timebetween data collection.

Advantageously, IDDSs allow for an infusion pump to provide dynamictiming and delivery rate adjustment settings of medication infusion flowrate(s) based on real-time observation of presented Parkinson's Diseasesymptom intensity levels. IDDSs according to the present disclosure maybe configured to continually monitor, analyze, and adjust the basal rateof infusion pump medication in response to observed patient motionfeedback. The observation may include monitoring body tremors, Dystonia,Dyskinesia, gait, and/or freezing as detected by one or more six-axismotion sensors.

Since each Parkinson's Disease patient throughout each stage of symptomsmay have individualized physical and/or cadence tremor characteristics,an advantageous feature of IDDSs according to the present disclosure isthe periodic recording and subsequent periodic matching and analysisbetween earlier individualized patterns and present patterns todetermine if pattern excursions have changed in any metric (e.g.intensity, duration, type of motion, etc.). In some embodiments, an IDDSmay function through a single wrist-worn sensor, typically on the bodyside presenting the greatest or more frequent abnormal symptoms. In someembodiments, an IDDS may function through two sensors, one on a wrist ofthe patient and one body worn for a greater number of data collectionpoints that communicates with the controller of the infusion pump tomake a determinations regarding pharmacological delivery rate changesbased on the data from one or both sensor controllers. In someembodiments, an IDDS may function through a wrist-worn sensors and anadditional body worn sensor for even more data collection points, e.g. asensor that is worn around the waist or torso of the user (such as asensor that comprises a belt or attaches to a belt). Variouscombinations of sensor numbers and locations may be used. In someembodiments, the drug infusion pump unit of the IDDS may have anintegrated gyroscopic and/or orientation position sensor to providepositional information and/or to supplement the data from other sensors.

The controller 14 may be configured to analyze body movements attributedto a “base” or starting point movement level, and from this base leveldetermination a pharmacological delivery rate is initiallyestablished/stored by the controller 14 with the movement data obtainedfrom the one or more worn sensors through sensor input channels of thecontroller 14. By analyzing this data, the controller 14 is configuredto generate an individualized baseline pharmacological delivery rate. Abaseline deviation ratio is selected which applies a percent rate changeto the pharmacological delivery rate (or other rate change, e.g.absolute volume rate change) to either increase or decrease the deliveryamount based on sensed excursion position changes detected by the one ormore worn sensors. Sensed positional data may normally be recorded andstored while the user is in a resting position to better filter out“noise” from the relevant positional data input channels. The controller14 may analyze a subset of the data input channels for subsequentcomparisons. Nevertheless, the system would continue to monitor allinput channels (and non-relevant movement) in order to equalize theoverall body movement context and prevent unnecessary data “noise” orinfluence.

The recording/analytical comparison interval periods may be selected bya clinician to reflect a meaningful or useful diagnostic comparisonperiod. However, the controller 14 may be configured to delay anyrecordings/analysis until the overall body movement context isdetermined to be the same as during a previous recording. This delaywould prevent data external to the body positional motion from falselyskewing the data collected.

In some embodiments, the IDDS is configured to determine whether amovement delta exists between a hand-worn sensor and a non-hand wornsensor (or reference sensor) on certain data input channels. Byutilizing one or more non-wrist-worn six-axis sensors (such as one thatmay be integrated into the pump itself) to compare against a wrist-wornsensor, this would provide useful “external” (to a wrist-worn sensor)positional information and would be used to null or filter out anyexternal to the body movement “noise”, such as a person bouncing up anddown while traveling in a car. If the same data input channels from botha wrist-worn sensor and a non-wrist sensor were detecting the same orsimilar data, the system or controller 14 would be able to determinethat the data was not being created by the body itself and, thus, shouldnot be attributed to a tremor. If just a wrist-worn sensor was sensingcertain movement data without a “check” sensor also supplying positionaldata, there would not be a way to determine whether sensed body movementwas separate from any external movement applied to the user and thus thedetected movement would always be validly considered organic movementgenerated by the user.

In addition to random abnormal body movements (which would be recorded),one of the most common Parkinson's Disease symptoms is a constanttremor. A constant tremor is a rhythmical movement typical in eachindividual with Parkinson's Disease and may have an individualdistinctive cadence. The individual distinctive cadence may beidentified by the controller 14 through repeated detections of themovement or by manual calibration and identification by the user orclinician in the IDDS, and the IDDS is configured to record itsexistence. By empirically matching an instant comparison of theexcursion limits of movements containing this cadence movement with areference recording, a reliable measure of the progression ofParkinson's disease may be established. Any recorded and stored cadenceexamples also serve to filter the desired cadence movement informationfrom normal lifestyle movement data that does not contain a knownrepetition rate to avoid extraneous data “noise.”

Timely reporting of movement changes to a clinician is of substantialimportance as it may indicate a needed change in dosage level ormedication brand or type. An advantageous aspect to IDDS embodimentsaccording to the present disclosure is the inclusion of outgoing datareporting capability. In other words, the IDDS is configured tocommunicate with a clinician computer system through wired or wirelesscommunication means. For example, the IDDS may be configured towirelessly connect with a cell phone of the user and cause the cellphone to transmit a message through cellular or internet network(s) to aclinician computer system. This allows the IDDS controller 14 uponsensing a continuing excessive (i.e. “out of range”) abnormality toautomatically contact a clinician. In some embodiments, the message tothe clinician computer system may be to merely communicate a report ofthe detected excessive changes to the clinician. However, in someembodiments, the IDDS has the added ability to be remotely adjusted so aclinician may make an immediate dosage change(s) during or after anyreporting session. The IDDS communications capability may include theuse of WI-FI, cellular, or other communications methods or protocols incommon use.

An IDDS according to the present disclosure may also have the capabilityto autonomously make dosage changes, to either increase or decrease, inresponse to detected symptomatic changes. The changes would be analyzedby the controller 14, for instance, on the basis of a patient exceedinga preset level of instant or averaged abnormal excursion increases, onthe basis of exceeding a preset level of abnormal excursion decreases, atime of day context, etc. The range of automatic dosage delivery changeswould be limited by a pre-set dosage (change) “collar” restraint withsuch autonomous limits determined by the clinician for each user.

Advantageously, an IDDS may be configured to continuously (or at desiredintervals) record relevant data, make periodic comparisons between thatdata and previous data records, and/or take action upon the detection ofphysiological significant data changes. The comparisons may includeindividual sequential recording comparisons, or an averaging andcomparison between timely groups of recordings.

In embodiments according to the present disclosure, the generation andstorage of data readings for later analysis and action goes beyond therecording of “simple” or “raw” motion data. For example, an IDDS may uselinked “metadata”, or data that links the primary motion data valueswithin context of other values which brings sub-context to the maindata. This allows, for instance, the historical “raw” motion data to bedirectly linked to additional information such as time/date stamps, thepharmacological delivery rate at the time of recording, the estimatedcirculating medicine level at the time of recording, the medicationtype, the medication strength, the body activity level at the time ofrecording, the sleep/awake state at the time of recording, etc. Thisallows the associated physical body movement data to provide a much moremeaningful and rich context. With such metadata, extensive reports,charts, and detailed analyses may be automatically generated for eachpatient, thereby creating a far more useful dynamic picture of dosageefficacy, etc. for each user/patient on an individualized level.

In some embodiments, an IDDS contains Artificial Intelligence (AI) whichlearns from the recording analysis of a user/patient relative to theresults of prior automatic dose correction actions. The system's motionand orientation sensor(s), in addition to monitoring abnormal bodymovements also function as a lifestyle input sensor(s) to determinewhether a person is sleeping, exercising, etc. Since individuals spendthe majority of their day in an upright position whether standing,sitting, etc., the IDDS controller 14 assumes that this is the normallifestyle position to differentiate against to determine whether a bodyis in a prone or resting/sleeping position.

As its name implies, IDDS is an intelligent drug delivery system that iscapable of learning when and if various lifestyle conditions need dosagealterations to occur. Based on the analysis of a patient's historicreaction (or lack of a reaction) to certain level changes of infusedmedication, an IDDS is capable of reporting to a patient's clinicianwhich dosage change(s) resulted in the most desirable balance betweenmitigation of symptoms and occurrence of side effects, and what minimuminfusion rate(s) were required to achieve the desired changes for thisbalance to occur at.

A determination of a specific lifestyle event change would bias theautomatic pharmacological delivery adjustment algorithm(s) to allow forappropriate remedial actions such as providing a non-planned medicationrate increase should the IDDS detect, for instance, a user awakeningfrom sleep, or reducing a medication basal rate should the IDDS detectthe user falling asleep, etc. The lifestyle detection also plays animportant role so as not to confuse the artificial intelligence, forinstance, between sensing a reduction in physical symptoms caused by amedicine's resultant therapeutic action and a user sleeping with itsinherent abnormal movement reduction typical of natural sleep paralysis.While people are typically creatures of habit and generally go to sleepabout the same time each day, there are numerous outside factors andcircumstances that can alter their schedule and dynamically require adosage change to occur. An IDDS overcomes the time of day limitations ofa conventional/non-intelligent continuous infusion pumps dosing rateschedule by allowing the IDDS to intelligently and dynamically base aninstant dosage rate change by reacting to real-time sensing andlifestyle event determination. If a selected periodic recording time wasreached and there was a data-skewing short-term lifestyle event takingplace (e.g., exercising) the system would automatically enter a “tryagain later” mode which would delay and prevent any atypical recordingor analysis, for instance, for a predetermined amount of time (e.g. 15minutes or whatever time frame is determined to be appropriate by thecontroller's artificial intelligence or selected from a preconfiguredamount of time selections).

An IDDS according to the present disclosure generates extendedhistorical sensor recorded data, from which dosage “cause and effect”record data is stored within the IDDS pump controller 14 itself (orother memory). The IDDS allows the data to be able to be remotelyaccessed at any time by one or more medical professionals for thepurpose of both clinically analyzing historic results, as well asallowing a post-analysis IDDS dosage change to be made remotely by aphysician/clinician. This feature is especially important for patientslocated in areas remote to medical care, as well as overcoming theprogressive inability of many Parkinson disease patients to be able totravel to a clinical setting. Even with clinical evaluations there istypically a progressive cognitive decline with Parkinson's diseasepatients that results in patients increasingly being unable to eitherobserve their own true medical condition and/or adequately respond tomedical questioning by a clinician. With an IDDS this problem isaddressed by having most or all of the relevant data able to becompletely accessible in an empirical fashion from among the storeddata.

By continually monitoring a patient's movements, an IDDS is capable ofnoticing and analyzing any clinically relevant changes in movementexcursions on an absolute basis, which for the first time allows a trulyobjective determination of Parkinson's disease progression to be madefor any given time frame. An IDDS can also monitor on an absoluteempirical basis the systemic and physiological effects of exercise,certain therapies, etc. on a patient so as to truly gauge whattherapeutic efforts are working effectively or not.

An IDDS also uniquely provides new opportunities for clinicians toperform proactive range-bound medication changes in order to preciselyand efficiently observe real-world cause and effect studies to determinethe optimal dosaging for each patient. For example, an IDDS may beconfigured to do automatic dosage bracketing of pharmacologicals toempirically ascertain an optimum dosage level(s). For a given timeperiod, which is selectable by a clinician, the IDDS will bias theoverall dosing by a selectable percentage above or below a currentbaseline. For example, the IDDS would skew the average dosing aselectable step lower and then make an analysis of any resultantabnormal movement symptoms presented during the altered/skewed dosageperiod and compare the data results to the prior baseline. If thesymptoms have generally stayed the same, the system would again repeatthe stepped dosage reduction and continue with this until an increase inpresented symptoms are noted and then return to the most recent previousdosage levels. The automatic bracketing may also be used in the oppositedosing direction to determine if there is symptom reduction with ahigher dosage regime. Although the previous example was directed to anoverall daily dosage study, an IDDS is also capable of automaticbracketing and analysis of just specific times of the day and/or days ofthe week. Since the metadata recorded by the IDDS includes thetype/brand or analog information of the medication that was beinginfused (e.g. either inputted by the user or the clinician), it isrelatively simple to conduct relative efficacy comparisons on anindividual patient (or for that matter a group of patients) toempirically determine which of the available medications provide thebest/most desired results as measured, for instance, by the total numberof recorded abnormal movements within the same time frame and relativedosage for each medication. Similarly, an IDDS is a valuable platformfor the empirical field-trial results of new Parkinson's medications aswell.

Advantageously, since the actual infusion pump as well as the motionsensors are designed to run on battery power, medication infusion aswell as tremor monitoring are able to continue despite commercial poweroutages or while traveling.

At times when the medication reservoir and infusion set are beingchanged, with one or more system components being taken offline to berecharged, etc., the infusion pump and/or the controller 14 would notethis activity and either exclude any medication delivery logginginformation from that time period's recorded data, or annotate the datafrom that time period via metadata so as not to falsely skew thesubsequent recorded data analysis.

At times when a body worn sensor is not being worn or the data from adevice is otherwise unavailable (e.g. the sensor is undergoing a batteryreplacement or recharge, etc.) the controller 14 (or infusion pumpcontroller) would discontinue its dynamic delivery rate determinationmethod and instead proceed/continue with the last known dynamic infusionrate(s). During periods of extended loss of sensor data, the IDDS mayoperate in accordance with self-generated historical time-contextualinfusion rates generated via recent pump activity. In the event the pumphas not had sufficient operating activity such as in the case with a newpump, the system may operate in accordance with various traditional timeof day non-dynamic dosage patterns that are typically based upon andaltered strictly according to a daily time schedule so as not to allow acomplete diminishment of circulating medication to occur. The IDDS wouldtypically include a processor with an integral real time clock builtinto the system or functional equivalent.

In some embodiments, each IDDS component are serialized to ensuremultiple sensors in a given area are uniquely linked to the appropriateserialized infusion pump, which would ensure data from a body wornsensor(s) is not mistakenly provided to a nearby infusion pump of adifferent user.

In some embodiments, the infusion pump of an IDDS allows for the releaseof both an active pharmacological medication as well as an adjunctiveprecursor medication (agonist) that IDDS would automatically andappropriately infuse prior to the infusion of the primarypharmacological agent.

In some embodiments, the IDDS continually calculates the level ofcirculating Levodopa (or other pharmacological agent) at any given timewith said calculation based on the infusion timing and dosing levels ofthe pharmacological agent and attaches that circulation level asmetadata. Additionally, the IDDS tracks and records the peak and minimumcirculating levels of said pharmacological for any given time frame inorder to be able to retroactively analyze these levels against thesensed abnormal movements such as tremors, freezing onset, dystonia,Gait abnormalities, and/or dyskinesia in the same time frame in order tofurther clinical evaluation. This essentially eliminates the need for apatient to maintain a “motor diary” which is of limited value relativeto IDDS in that it cannot provide critical details such as thecirculating level of a pharmaceutical during each episode or for anygiven time.

In some embodiments, an overall goal of the IDDS is to provide anoptimized level of medication that minimizes or curtails instant tremorsor other body motion abnormalities. In the context of Parkinson'sDisease control, an optimized level of medication provides for just theminimum level of daily medication infusion that is consistent withabnormal motion control in order to avoid over-medicating the patient.Without dynamic dosing control, it is inevitable that excess medicationdosaging will occur at times, along with times of sub-optimal medicationlevels. This results in “off” periods wherein insufficientpharmacological levels cause a lack of abnormal movement control.Without a dynamic control of pharmacological levels as provided by IDDS,inevitably there are numerous times when the pharmacological levels aremuch higher than are necessary, with this hyper-dosing causing furthercomplications such as the onset of dyskinesia.

A body worn sensor may be adhesively attached to a body in order toensure the sensor orientation is indicative of a certain bodyorientation.

IDDS may also include an ambient light sensor to further aid in thedetection of a lifestyle event such as sleeping.

The use of a wireless charging grid that may be an integral part of ablanket, bed-sheet, etc. may allow wireless charging of the controllerand/or body sensors.

The IDDS controller may employ Digital Signal Processing (DSP) in itsdetermination of position and/or acceleration data results.

IDDS provides for dosage delivered feedback from a infusion pump to thecontroller.

Referring to FIG. 7, a flow diagram 90 shows an exemplary method ofoperation of the IDDS of FIGS. 1 and 2 in accordance with embodiments ofthe present disclosure. At block 92, the controller 14 monitors formotion data received from one or more of the six-axis accelerometersensors 16, 25 and for orientation data from one or more of the pitchsensors 18, 26. At block 94, the controller 14 determines if motion dataand/or orientation data has changed. If the controller 14 determinesthat there is no change in the motion data and/or orientation data, thenthe controller 14 returns to block 92 for monitoring. If the controller14 determines there is a change in motion data and/or orientation data,the controller 14 proceeds to block 96 where the change in motion dataand/or orientation data is analyzed by the controller 14. The controller14 proceeds to block 98 where the controller 14 compares the motion dataand orientation data, which may be time-weighted, with previouslyrecorded motion data and orientation data of the user associated withabnormal behavior movements (or tremor incidents) or compares the datawith other baseline data, in order to determine an intensity and/orfrequency of abnormal movements. If the controller 14 determines thatthe intensity and/or frequency of the abnormal movements are within apredetermined threshold and/or have not changed by a predeterminedamount (or percentage) from previous data of the user (or baseline data)then the controller 14 determines that the current dosage regimen of thecurrent pharmacological drug should be maintained and the controller 14proceeds to block 100. At block 100, the controller 14 generates a pumpinstruction signal to continue or maintain the current dosage regimen,e.g. by maintaining the current delivery rate of the pharmacologicaldrug.

If the controller 14 determines that the intensity and/or frequency isabove a predetermined threshold, or if the intensity and/or frequencyhas increased from previous data (or other baseline data) beyond apredetermined amount (or percentage), then the controller 14 determinesthat the dosage regimen needs to be increased and the controller 14proceeds to block 102. At block 102, the controller 14 generates a pumpinstruction signal to increase the dosage regimen, e.g. by increasingthe current delivery rate of the pharmacological drug.

If the controller 14 determines that the intensity and/or frequency isbelow a predetermined threshold, or if the intensity and/or frequencyhas decreased from previous data (or other baseline data) beyond apredetermined amount (or percentage), then the controller 14 determinesthat the dosage regimen may be decreased or that it is an appropriatetime to perform dosage bracketing and the controller 14 proceeds toblock 104. At block 104, the controller 14 generates a pump instructionsignal to decrease the dosage regimen and/or perform bracketing, e.g. bydecreasing the current delivery rate of the pharmacological drug.

Following blocks 100, 102 and 104, the controller 14 proceeds tomonitoring motion and orientation data at block 92 in order to repeatthe above described process or to perform dosage bracketing as discussedabove.

An advantage of IDDSs according to the present disclosure is the abilityto provide long-term recording storage, and analysis of data involvingthe relationship between observed abnormal body movements within thecontext of historical medication dose(s) that was delivered to a patientin order to precisely observe the instantaneous dosage requirements of apatient with the historically delivered medication rate(s) of aninfusion pump while also tracking and observing any trending progressionof the disease.

In certain embodiments, the motion sensors of an IPAS enabled insulinpump serve as an adjunct to the direct continuous measurement of atrackable value (e.g. blood glucose) to augment the insulin pumpdelivery rate in response to the existence of a “Lifestyle” event whilein some embodiments the motion sensors in an IDDS serve as the primarymeans of controlling the delivery rate of medication.

The foregoing description of embodiments of the present disclosure hasbeen presented for the purpose of illustration and description. It isnot intended to be exhaustive or to limit the invention to the formdisclosed. While the exemplary application has focused on the treatmentof Parkinson's disease, with minor programming and/or hardwaremodification, both the monitoring and closed-loop capability of IDDS mayalso be used with other neurological conditions and treatment, thusobvious modifications and variations are possible in light of the abovedisclosure and should be considered to be within the scope and spirit ofthe present disclosure. The embodiments described were chosen to bestillustrate the principles of the invention and practical applicationsthereof to enable one of ordinary skill in the art to utilize theinvention in various embodiments and with various modifications assuited to the particular use contemplated.

What is claimed is:
 1. An intelligent drug delivery system comprising: adevice body; an accelerometer sensor arranged in or on the device bodyand configured to output multi-axis motion data based on detectedmulti-axis motion and acceleration; a gyroscopic pitch sensor arrangedin or on the device body and configured to output orientation data basedon detected orientation; a controller in communication with theaccelerometer sensor and the gyroscopic pitch sensor; wherein, based onthe multi-axis motion data and/or the orientation data, the controlleris configured to determine positional movements of the device body,speed of movement of the device body, linger times of the device body,and/or multi-axial cadence of movement of the device body; wherein thecontroller is configured to generate a pump instruction signal based onthe determined multi-axial movement of the device body, multi-axialspeed and acceleration of the device body, linger times of the devicebody, and/or multi-axial cadence of movement of the device body, thepump instruction signal including an instruction to change or suspend adelivery rate of a pharmacological material.
 2. The intelligent drugdelivery system according to claim 1, wherein the pharmacologicalmaterial is configured to treat one or more symptoms of Parkinson'sdisease.
 3. The intelligent drug delivery system according to claim 1,further comprising a memory, wherein the controller is configured tostore in the memory the multi-axis motion data and the orientation datareceived by the controller.
 4. The intelligent drug delivery systemaccording to claim 3, wherein the controller continually stores recordsof positional locations and/or multi-axial positional movements of thedevice body in the memory as recorded movement data.
 5. The intelligentdrug delivery system according to claim 4, wherein the recordedmulti-axis movement data is associated with relevant metadata.
 6. Theintelligent drug delivery system according to claim 5, wherein themetadata comprises information about the brand and/or type of thepharmacological drug.
 7. The intelligent drug delivery system accordingto claim 6, wherein the metadata comprises information about thestrength of the pharmacological drug.
 8. The intelligent drug deliverysystem according to claim 5, wherein the data is analyzed to determinethe state of a lifestyle activity level of a user or an indication ordetermination as to whether the user is awake or asleep.
 9. Theintelligent drug delivery system according to claim 4, wherein therecorded movement data is linked to multiple metadata information. 10.The intelligent drug delivery system according to claim 4, wherein therecorded movement data and associated metadata is configured to beremotely accessed by a clinician remote computer.
 11. The intelligentdrug delivery system according to claim 9, wherein the pump instructionsignal is configured to be modified by the clinician remote computer.12. The intelligent drug delivery system according to claim 4, whereinthe controller continually stores records the delivery rate and changesto the delivery rate in the memory as drug delivery data.
 13. Theintelligent drug delivery system according to claim 4, wherein therecorded movement data and the drug delivery data is configured to beremotely accessed by a clinician remote computer.
 14. The intelligentdrug delivery system according to claim 13, wherein the pump instructionsignal is configured to be modified by the clinician remote computer.15. The intelligent drug delivery system according to claim 1, whereinthe controller is configured to generate the pump instruction signal toreduce the delivery rate of the pharmacological material for apredetermined amount of time, wherein the controller is configured todetermine whether there are any multi-axial changes in frequency and/orintensity in speed of the device body, linger times of the device body,and/or multi-axial cadence of movement of the device body during thepredetermined amount of time compared to movement recorded prior to thepredetermined amount of time, and wherein, if the changes are above apredetermined threshold, the controller is configured to generate a newpump instruction signal to restore the delivery rate of thepharmacological material to the delivery rate prior to the reduction.16. The intelligent drug delivery system according to claim 1, whereinthe controller is configured to calculate instant pharmacologicalcirculation levels for a selected time period and compare the calculatedlevels to a prior time period, wherein the pump instruction signal isfurther based on the comparison.
 17. The intelligent drug deliverysystem according to claim 1, wherein the accelerometer sensor is aminimum six-axis accelerometer sensor.
 18. An intelligent drug deliverysystem comprising: a first multi-axial device body and a secondmulti-axial device body; a first multi-axial accelerometer sensorarranged in or on the first device body and configured to output firstmulti-axial motion data based on detected multi-axial motion, and asecond multi-axial accelerometer sensor arranged in or on the seconddevice body and configured to output second multi-axial motion databased on detected multi-axial motion; a first multi-axial gyroscopicpitch sensor arranged in or on the first device body and configured tooutput first multi-axial orientation data based on detected multi-axialorientation, and a second multi-axial gyroscopic pitch sensor arrangedin or on the second device body and configured to output secondmulti-axial orientation data based on detected multi-axial orientation;a controller in communication with the first multi-axial accelerometersensor, the second multi-axial accelerometer sensor, the firstmulti-axial gyroscopic pitch sensor, and the second multi-axialgyroscopic pitch sensor; wherein the controller is configured todetermine multi-axial positional locations of the first and secondmulti-axial device bodies, speed of the first and second multi-axialdevice bodies, linger times of the first and second device bodies,and/or cadence of multi-axial movement of the first and second devicebodies; wherein the controller is configured to generate a pumpinstruction signal based on the first multi-axial motion data, firstmulti-axial orientation data, second multi-axial motion data and/or thesecond multi-axial orientation data, the pump instruction signalincluding an instruction to change or suspend a delivery rate of apharmacological material.
 19. The intelligent drug delivery systemaccording to claim 18, wherein the first multi-axial device body and thesecond multi-axial device body are each configured to be worn on arespective wrist of a user.
 20. The intelligent drug delivery systemaccording to claim 19, further comprising a multi-axis body sensor,configured to be worn on the body of the user.
 21. A method ofintelligently delivering a drug to a Parkinson's disease patientcomprising: monitoring, by a controller, multi-axial motion data and/ormulti-axial orientation data generated by one or more multi-axis sensorsarranged within or on a device body worn by a user with Parkinson'sdisease; determining, by the controller, multi-axial positionallocations of a device body, multi-axial speed and acceleration of thedevice body, linger times of the device body, and/or multi-axial cadenceof movement of the device body based on the multi-axial motion dataand/or the orientation data; generating, by the controller, a pumpinstruction signal based on the multi-axial motion data and/ororientation data, the pump instruction signal including an instructionto change or suspend a pharmacological material delivery rate of aninfusion pump; wherein the pharmacological material is configured totreat one or more symptoms of Parkinson's disease.